A Wall-E Inspired Platform Map

Where Agents Can Scale

29 platform capabilities. Three agent modes. One question: Is your documentation clear enough for agents to act safely?

"Your agents can't ask 'is this still valid?' — that's your job."

Wall-E compacts trash long after the context changes. AUTO enforces a stale directive. They're not broken — they're literal. The question is whether your directives are still true.

EXECUTIVE SUMMARY
1
Where you can safely Execute
Green cells = let agents act autonomously. Low blast radius, high reversibility.
2
Where you must keep Propose+Review
Amber cells = agents draft, humans approve. GitOps provides the safety net.
3
Where to invest (and why)
Blue cells = semantic debt. Fix the vocabulary gap before automating.
🤖 Wall-E Territory — Safe to run
🛸 EVE Territory — Propose, then confirm
🧭 Captain Territory — Invest to unlock
0
Ready Now
0
Emerging
0
Invest to Unlock
87
Total Cells

How to Read This Map

15 seconds
The Grid
Rows = Capability domains
Columns = Agent modes (Assist → Propose → Execute)
Selector = Your platform maturity level
The Colors
Green = Go. Guardrails in place, safe to scale.
Amber = Slow. Works, but add review gates.
Blue = Stop. Fix vocabulary/docs first.
→ Start Here
  1. 1. Select your maturity level
  2. 2. Find your blue cells (your gaps)
  3. 3. Hover any cell for context
  4. 4. Use the scoring rubric below
CYBERNETIC CONTROL LOOP

How Agents Think

An intelligent agent uses sensors and actuators to interact with their environment, and makes decisions about which actions to take in order to achieve certain outcomes.

Agent uses Sensors to collect Percepts are information about the Current State of the Environment may be stored as Previous States Actuators to perform Actions affect the makes Decisions dictated by Models are representations of the fed into Rules Goals Utility connect actions to percepts are desirable evaluates Outcome(s) for the
Node Title

Description goes here

Hover over nodes to explore • Based on Dubberly Design Office / Russell & Norvig

PRODUCTION MAPPING

CNCF Technical Cross-Reference

The conceptual components from cybernetic theory map directly to production-grade CNCF graduated projects. This isn't metaphor — it's architecture.

Role Conceptual Function CNCF Technology What It Does
Sensor
Percept Collection
OpenTelemetry Cilium (eBPF)
Collects traces, metrics, and logs from the environment
Goal
Desired Outcome
Git Manifests SLOs
Defines the target state or performance objective
Comparator
Policy Evaluation
OPA Kyverno
Evaluates current state against policy and goals
Actuator
Action Execution
K8s Control Plane Crossplane
Executes changes to bring system toward goal state
Model
Historical State
etcd Prometheus
Stores world state and time-series for prediction

The insight: Every CNCF project plays a specific role in the cybernetic loop. When you deploy Prometheus, you're adding a Model. When you add OPA, you're inserting a Comparator. Understanding these roles helps you reason about what's missing in your control system.

PLATFORM MATURITY LEVEL

Select your maturity to watch the map transform

The Mental Model

Three characters. Three levels of autonomy. One question: is the directive still valid?

🤖

Wall-E Territory

Reliable, consistent, trustworthy. Low blast radius. Safe to let run.

Safe for automation
🛸

EVE Territory

Focused, purposeful. Needs clear scope. Propose, then confirm.

Needs human review
🧭

Captain Territory

Context required. The human who knows what it means. Invest to unlock.

Requires investment

These characters map to agent theory. Wall-E territory works because simple reflex agents are sufficient — no planning needed. EVE territory requires goal-based agents that understand intent. Captain territory demands model-based or learning agents that can predict and adapt.

Character
Readiness
Agent Class Required
Why This Pairing
🤖 Wall-E
Ready
Simple Reflex
Stable rules, low variance. "If X, do Y" is enough.
🛸 EVE
Emerging
Goal-Based
Needs intent awareness. "Reach state Y" requires planning.
🧭 Captain
Invest
Model-Based / Learning
Requires prediction and adaptation. Not yet safe to automate.
AUTONOMY:
Assist
Assist Mode

Agent watches and surfaces insights. Human drives. Low risk — agent can't change anything.

Example: "Show me slow queries" or "Flag risky PRs"
Propose
Propose Mode

Agent drafts changes for human approval. GitOps provides safety net. Medium risk — requires review gate.

Example: "Draft a fix for this bug" or "Suggest config change"
Execute
Execute Mode

Agent acts autonomously. Higher risk — requires strong semantic coherence and rollback capability.

Example: "Auto-scale based on traffic" or "Auto-remediate alerts"
PACE:
Fast
Fast Pace (Weeks)

Changes frequently. Low blast radius. Safe for agent experimentation.

Medium
Medium Pace (Months)

Changes quarterly. Moderate blast radius. Agent proposals work well.

Slow
Slow Pace (Quarters)

Changes rarely. High blast radius. Agent assist valuable, execution risky.

Cross
Cross-Cutting

Spans multiple pace layers. Changes cascade unpredictably.

Hover over any term for details

The Five Ceremonies

Every product org runs on recurring rituals — standups, retros, planning, demos. These ceremonies fall into four categories. But there's a fifth one missing. And it's why your agents keep hitting walls.

CEREMONY 01

Discovery & Planning

🤖 Agent Ready

Backlog grooming, sprint planning, user research synthesis, competitive analysis.

Why agents work here: Low stakes, reversible outputs.
CEREMONY 02

Delivery & Iteration

🛸 Emerging

Standups, code review, CI/CD, deployments, hotfixes, feature flags.

Why agents are close: GitOps provides guardrails.
CEREMONY 03

Strategy & Alignment

🧭 Captain Territory

OKR setting, roadmap reviews, resource allocation, priority calls, reorgs.

Why agents struggle: These decisions require context that isn't written down.
CEREMONY 04

Learning & Validation

🤖 Agent Ready

Retros, post-mortems, A/B test analysis, metrics reviews, customer feedback loops.

Why agents work here: Data-heavy, pattern-matching territory.
CEREMONY 05

Semantic Maintenance

The ceremony nobody runs. Explicitly maintaining the shared vocabulary — what "production-ready" means, what "customer" refers to, why this service exists, what "done" looks like for this team.

When this degrades, humans can still navigate — they ask questions, read between lines, ping Slack. Agents can't. They take your stale docs literally.

📖
Glossary reviews
🔍
Doc-to-reality audits
🤝
Cross-team vocab sync

This is the unlock. Run this ceremony, and blue cells start turning amber. Amber turns green. Your agents stop being AUTO and start being Wall-E.

🌱

The Captain Updated the Directive

At the end of Wall-E, the captain doesn't defeat AUTO with a better algorithm. He sees the plant, understands what it means, and decides the old instruction no longer applies.

That's the work. Not faster agents, not cleverer prompts — but keeping the meaning behind the directive aligned with reality.

Read the Framework

The missing ceremony: Semantic Maintenance — regularly checking that your language still matches your reality.

Wall-E kept compacting long after everyone left. Your agents will keep executing long after the context changes.